Descriptive analysis of computational methods for automating mammograms with practical applications

TL;DR

This paper provides a comprehensive overview of computational methods for digital mammogram analysis, highlighting advancements in image processing, feature extraction, and applications to aid early breast cancer detection and related tasks.

Contribution

It offers a descriptive survey of recent computational techniques and benchmarks in digital mammography, serving as a guide for future research and practical applications.

Findings

Overview of image pre-processing techniques

Discussion of feature extraction methods

Development of benchmark datasets

Abstract

Mammography is a vital screening technique for early revealing and identification of breast cancer in order to assist to decrease mortality rate. Practical applications of mammograms are not limited to breast cancer revealing, identification ,but include task based lens design, image compression, image classification, content based image retrieval and a host of others. Mammography computational analysis methods are a useful tool for specialists to reveal hidden features and extract significant information in mammograms. Digital mammograms are mammography images available along with the conventional screen-film mammography to make automation of mammograms easier. In this paper, we descriptively discuss computational advancement in digital mammograms to serve as a compass for research and practice in the domain of computational mammography and related fields. The discussion focuses on research aiming at a variety of applications and automations of mammograms. It covers different perspectives on image pre-processing, feature extraction, application of mammograms, screen-film mammogram, digital mammogram and development of benchmark corpora for experimenting with digital mammograms.